Crop phenotype is usually expressed in terms of characteristics like plant height, leaf architecture and leaf area index (LAI). In the case of maize, stalk diameter is seldom quantified because its measurement does not readily lend itself to automation. Justification for automating the measurement of stalk diameter and plant spacing is based on the finding that stalk diameter was able to account for about 65% of the variability in maize yield per plant in three irrigated field studies. A high-speed reflectance sensor and simulation apparatus was developed to explore the potential for automating maize stalk diameter assessment. The prototyped system accurately measured both stalk diameter and plant spacing in the laboratory at simulated velocities up to 12 km/h.

This experiment aimed to assess the effect of different indoor winter growth rates (WGR) followed by different concentrate supplementation levels at pasture on meat quality of 90 bulls. During the first winter, bulls were offered grass silage ad libitum and either 3 kg (WGR3) or 6 kg (WGR6) of concentrates. After turn-out to pasture, bulls were offered: grass without supplementation (PO), grass plus 0.2 predicted dry matter intake (DMI) as concentrates (PL) or grass plus 0.4 predicted DMI as concentrates (PH). After finishing, colour, chemical composition (unaged), instrumental texture and sensory characteristics (14 days of ageing) of longissimus thoracis were measured. WGR6 bulls had heavier carcasses than WGR3 bulls. There was an interaction between WGR and supplementation for instrumental texture and redness (a). Within WGR3, PO beef was the most tender, whereas within WRG6, PL was the most tender. However, these differences were not detected by the sensory panel. Within WGR3, redness was the lowest for PL, whereas within WRG6, PO was the least red. No differences were found for chemical composition. The multivariate analysis highlighted WGR as the main variable affecting meat quality characteristics. In conclusion, variations in growth path exerted minor effects on appearance and instrumental texture which did not affect the perception of bull beef by a trained sensory panel.

The aim of this study was to analyse cow reproductive performance on 37 Irish suckler beef farms and determine how reproductive efficiency influences farm profitability. The main reproductive factors associated with gross output value per livestock unit (GO/LU) were average age at first calving (r=−0.19, P<0.01) and number of months with a calving (r=−0.15, P<0.05). A 1 month increase in average age at first calving was shown to reduce GO/LU by €14 across suckler farms. Average age at first calving was positively correlated with calving interval (r=0.21, P<0.001) and the number of months with a calving (r=0.18, P<0.01). Number of months with a calving was also positively correlated with calf mortality (r=0.21, P<0.01). However, these relationships between reproductive variables had no statistically significant impact on farm financial performance. It is therefore concluded that additional analysis at animal level is required to determine key reproductive indicators contributing to farm profitability.

The human population is expected to reach 9 billion by 2050 and thus high yield crop varieties need to be developed. Remote sensing can estimate crop parameters non-destructively and quickly. The aim of this study was to compare and evaluate the use of a commercial RGB camera with an expensive canopy sensor in the crop development of two legumes. The RGB camera based vegetation index (NGRDI) was compared with the canopy sensor derived vegetation indices (NDVI and NDRE) for estimating legume crop growth parameters. The results indicated that the use of a simple digital camera RGB can in some cases replace spectral canopy sensors.

This study aimed to evaluate levels of beef cow fertility using calving interval (CI; measured in days) as a measure, and investigate the effects of breed, season, year and progeny gender on CI. The CI data included 273 764 records collected between 1997 and 2012 and included the seven most common breeds (and their crosses) in Northern Ireland (Charolais, Limousin, Belgian Blue, Simmental, Blonde d’Aquitaine, Aberdeen Angus and Hereford), accounting for 94.1% of beef dams recorded. Mean CI for all cows was 395 days, 30 days longer than the optimum 365 days. Charolais and Belgian Blue dams had the longest CI (P<0.05). Cows older than 144 months had a longer CI (P<0.05) compared with cows younger than 144 months. Charolais sires had a shorter subsequent CI of 392 days (P<0.05) compared with the other breeds. Cows calving in June had the shortest subsequent CI (376 days; P<0.05), whereas cows calving in November had the longest subsequent CI (410 days). Progeny gender did not significantly affect CI. This study establishes the level of beef cow fertility using CI as a measure in Northern Ireland is sub optimal and there are opportunities for improvement. Factors identified as influencing CI included dam breed, sire breed and month of parturition. This knowledge can be used to direct breeding programmes and inform knowledge transfer protocol to improve sustainability of beef production.

The research concerns the changes of spectral characteristics of reflected radiation (360 to 1 000 nm) of spring wheat leaves under nitrogen deficiency and moderate soil drought. The efficiency of factorial influence (η2) on chlorophyll index was equal to 20% and 4% under nitrogen and water deficiency, respectively. Most significantly soil drought influenced the water index WRI (η2=55%) and the light diffusion index R800 (η2=28%), which was caused by changes in leaf structure. At low levels of nitrogen supply, these parameters did not change or changed only slightly (η2=2%). It may be deduced that the data base for crop monitoring in precision farming systems must contain a series of optical criteria for assessing specific and non-specific changes in optical characteristics of a crop canopy under the impact of various stress factors.

Accurately quantifying a consumer’s willingness to pay (WTP) for beef of different eating qualities is intrinsically linked to the development of eating-quality-based meat grading systems, and therefore the delivery of consistent, quality beef to the consumer. Following Australian MSA (Meat Standards Australia) testing protocols, over 19 000 consumers from Northern Ireland, Poland, Ireland, France and Australia were asked to detail their willingness to pay for beef from one of four categories that best described the sample; unsatisfactory, good-every-day, better-than-every-day or premium quality. These figures were subsequently converted to a proportion relative to the good-every-day category (P-WTP) to allow comparison between different currencies and time periods. Consumers also answered a short demographic questionnaire. Consumer P-WTP was found to be remarkably consistent between different demographic groups. After quality grade, by far the greatest influence on P-WTP was country of origin. This difference was unable to be explained by the other demographic factors examined in this study, such as occupation, gender, frequency of consumption and the importance of beef in the diet. Therefore, we can conclude that the P-WTP for beef is highly transferrable between different consumer groups, but not countries.

The use of plant growth regulators (PGRs) on winter wheat in the UK is a common practice. Significant yield benefits can be gained from its use. For the most efficient use of PGRs the rates of application must be matched to the crop canopy. The variation of crop growth was monitored during the spring growing season using vegetation indices derived from satellite imagery. Application rates of PGRs were matched to changes in biomass as measured by the imagery. Final yields were measured using a GPS combine yield meter. The variable application showed an average yield benefit of 0.4 t/ha over the uniform full recommended application rate.

Genomic imprinting is a term applied to an epigenetic phenomenon where alleles are fully or partially inactivated depending on their parental origin. The relevance of this kind of parent-of-origin effects (POEs) for agriculturally important traits is widely known. A model (imprinting model) with a transmitting ability (TA) as sire and a TA as dam has been proposed that provides an estimate of the imprinting variance, whether the allelic inactivation is maternal, paternal, full or partial. Although the model can be used in a reduced version, large data sets still prevent the imprinting variance from being estimated. To further reduce the size of the equation system, we developed a parsimonious imprinting model with genetic effects for male ancestors only, as the TA as dam is replaced by her father’s TA. This parsimonious model was applied to 1 366 160 Simmental fattening bulls with slaughter data available on killing out percentage, net BW gain, EUROP class and fat score in a linear and generalised linear version. The pedigrees contained up to 2 637 761 ancestors. Proportion of the total genetic variance attributed to POEs ranged between 8.6% and 17.0%. On average, the maternal gamete accounted for the greater proportion of the imprinting variance. An equivalent version of the parsimonious model facilitated the estimation of POEs with reliabilities ranging between zero and 0.97.

The detection and identification of plant diseases is a fundamental task in sustainable crop production. An accurate estimate of disease incidence, disease severity and negative effects on yield quality and quantity is important for precision crop production, horticulture, plant breeding or fungicide screening as well as in basic and applied plant research. Particularly hyperspectral imaging of diseased plants offers insight into processes during pathogenesis. By hyperspectral imaging and subsequent data analysis routines, it was possible to realize an early detection, identification and quantification of different relevant plant diseases. Depending on the measuring scale, even subtle processes of defence and resistance mechanism of plants could be evaluated. Within this scope, recent results from studies in barley, wheat and sugar beet and their relevant foliar diseases will be presented.

Many plant diseases have distinct visual symptoms which can be used to identify and classify them correctly. This paper presents a potato disease classification algorithm which leverages these distinct appearances and the recent advances in computer vision made possible by deep learning. The algorithm uses a deep convolutional neural network training it to classify the tubers into five classes, four diseases classes and a healthy potato class. The database of images used in this study, containing potatoes of different shapes, sizes and diseases, was acquired, classified, and labelled manually by experts. The models were trained over different train-test splits to better understand the amount of image data needed to apply deep learning for such classification tasks.

The precise application of pesticides to fruit crops requires information regarding the tree or vine canopy as a system input in order to control the amount of liquid and air being applied. Variations in canopy volume and density occur due to variety, trellis system, growth stage, training system and season. Current practice is to occasionally change liquid volume but seldom to change airflow. This paper details the development and validation of an ultrasonic sensor system to measure not only canopy volume but also canopy density and presence. Sensors fitted to the sprayer can record, in real time, changes in crop characteristics as the sprayer moves along the row. Signals can then send information to variable output nozzles and adjustable air fans. Trials have been conducted and results have proven to be extremely reliable and accurate. The ability to precisely control the spray results in the optimum application rate, leading to better results in the use of pesticides, less environmental pollution (less drift and less leaf runoff) and improved economic viability for the fruit grower.

In agriculture, tank mixtures with several plant protection products (PPP) are frequently used, which makes it impossible to apply specific plant protection products to specific areas. Therefore a prototype of a field sprayer with a direct injection system was developed as part of a joint project between the company Herbert Dammann GmbH and the Julius Kühn- Institute. This framework enables a real-time site-specific dosing of plant protection products and water from separate containers with direct mixing before the application without residual amounts. Results show a high accuracy in the working range of the selected dosing pumps by means of test bench measurements and field tests Field tests for site-specific weed control in winter wheat demonstrate the applicability of the system under practical conditions and enable a precise application of various herbicides.

Assessment of Tar Spot Complex (TSC) severity in maize breeding experiments is conducted visually and may sometimes result in inconsistencies due to human interpretation. Disease scoring using remote sensing technologies may help bring more precision to the phenotyping process. An experiment for assessment of grain yield losses due to TSC was conducted at the Aguafria Experimental Station of the International Center for Wheat and Maize Improvement – CIMMYT in Mexico. Twenty-five maize genotypes were planted in spring of 2016 under a fungicide treatment to control TSC development and no fungicide treatment in a square lattice design with three replications. Four flights were carried out using an Unmanned Aerial Vehicle (UAV) equipped with a multispectral (550, 660, 735, 790 nm) and a thermal camera, simultaneously with the visual disease scorings and the yield was measured after harvesting. The preliminary results of the study indicated that the use of remote sensing in disease resistance phenotyping may be as effective as visual disease scoring since both correlate highly with the grain yield. Structural and chlorophyll vegetation indices (VIs) proved to be a good alternative for the estimation of yield losses caused by TSC in experimental field conditions, which may be potentially used for screening for resistance to this disease in maize genotypes, hypothetically reducing the need for visual disease scoring in the field.

A ground-based hyperspectral imaging system covering the spectral range of 384–1034 nm was used for Sclerotinia Stem Rot (SSR) detection. Two sample sets of oilseed leaves were collected. Four vegetation indices were extracted and evaluated by analysis of variance (ANOVA) combined with linear discriminant analysis (LDA) for the two sample sets. Discriminant models were built using the 4 vegetation indices. The discriminant results of the two sample sets were good with classification accuracies of the calibration set and the prediction set over 85%. The overall results indicated that vegetation indices calculated from ground-based hyperspectral imaging could be used as reliable and accurate indices for SSR detection.

In Spain, the use of annual cover crops is a crop management practice for irrigated vineyards that allows controlling vineyard vigor and yield, which also leads to improve the crop quality. Recently, Cynodon dactylon (bermudagrass) has been reported to infest those cover crops and colonize the grapevine rows, resulting in significant yield and economic losses due to the competition for water and nutrients. From timely unmanned aerial vehicle (UAV) imagery, the objective of this research was to map C. dactylon patches in order to provide an optimized site-specific weed management. A quadrocopter UAV equipped with a point-and-shoot camera was used to collect a set of aerial red-green-blue (RGB) images over a commercial vineyard plot, coinciding with the dormant period of C. dactylon (February 2016). Object-based image analysis (OBIA) techniques were used to develop an innovative algorithm for early discrimination and mapping of C. dactylon, which had the ability to solve the limitation of spectral similarity of this weed with cover crops or bare soil. As a general result, the classified maps of the studied vineyard showed four main classes, i.e. vine, cover crop, C. dactylon and bare soil, with 85% overall accuracy. These weed maps allow developing new strategies for site-specific control of C. dactylon populations in the context of precision viticulture.

The goal of the project was to supply growers with knowledge on how incorporation of machine vision technology can affect the wild blueberry crop, disease pressures, and the overall savings of select agrochemical inputs. A machine vision system was developed and mounted on a rear sprayer boom in front of the sprayer nozzles capable of targeting the agrochemical application on an as-needed basis. Results showed that plants that received the proper fungicide application were less prone to premature leaf drop resulting in larger stem diameters and higher bud counts and harvestable fruit yield. Fungicide application savings using the smart sprayer for spot-application was 12% as compared to a uniform application.

Soil application of herbicides for preemergence (PRE) weed control in grain sorghum is vital to control weeds. Efficacy of soil-applied herbicides is impacted by herbicide adsorption which is influenced by soil organic matter (SOM) and texture. With precision agriculture technologies, variable rate applications (VRA) can be utilized to maximize herbicide effectiveness. In 2016, algorithms were developed for two locations to use VRA of two tank mixed herbicides based on SOM and soil electrical conductivity (EC) collected by a Veris MSP3 system. Drone imagery provided an effective way to evaluate the efficacy of herbicide applications along with visual assessment. VRA applications of herbicide tank mixes provided equal weed control compared to flat rate applications.